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Use MCP and REST endpoints for AI-assisted coding

You can use AI-assisted authoring tools without installing docassemble.ALWeaver or docassemble.ALDashboard in your local Python environment.

Instead, call the APIs on a running Docassemble server:

  • ALWeaver REST API (/al/api/v1/weaver...)
  • ALDashboard REST API (/al/api/v1/dashboard/...)
  • ALDashboard MCP bridge (/al/api/v1/mcp)

Why use APIs instead of local package installs?​

  • Keeps your AI workflow lightweight on local machines.
  • Lets your AI agent work against one shared, server-side environment.
  • Avoids local dependency setup for OCR, PDF tooling, and Docassemble internals.
  • Makes async jobs available for longer-running tasks.

Authentication​

These endpoints use Docassemble API auth (api_verify()), typically:

  • X-API-Key: YOUR_API_KEY, or
  • Authorization: Bearer ... (if configured on your server).

Option 1: MCP bridge (tool discovery + tool execution)​

The MCP bridge is exposed by ALDashboard:

  • POST /al/api/v1/mcp (JSON-RPC 2.0)
  • GET /al/api/v1/mcp (metadata)
  • GET /al/api/v1/mcp/tools (tool list)
  • GET /al/api/v1/mcp/docs (human docs)

Supported methods:

  • initialize
  • ping
  • tools/list
  • tools/call

Use this when your AI coding tool can speak MCP or JSON-RPC and you want dynamic tool discovery.

Example: list MCP tools​

curl -X POST "https://YOURSERVER/al/api/v1/mcp" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

Example: call a discovered tool​

curl -X POST "https://YOURSERVER/al/api/v1/mcp" \
  -H "Content-Type: application/json" \
  -H "X-API-Key: YOUR_API_KEY" \
  -d '{"jsonrpc":"2.0","id":2,"method":"tools/call","params":{"name":"aldashboard.get_al_api_v1_dashboard_openapi_json","arguments":{}}}'

tools/call reuses the same request authentication context, so you do not need a second API key just for MCP.

Option 2: Direct REST calls​

Use REST directly when you want explicit control over endpoints and payloads.

ALWeaver API​

  • POST /al/api/v1/weaver
  • GET /al/api/v1/weaver/jobs/{job_id}
  • DELETE /al/api/v1/weaver/jobs/{job_id}
  • GET /al/api/v1/weaver/openapi.json
  • GET /al/api/v1/weaver/docs

ALDashboard API​

Examples include:

  • POST /al/api/v1/dashboard/docx/auto-label
  • POST /al/api/v1/dashboard/docx/relabel
  • POST /al/api/v1/dashboard/pdf/fields/detect
  • POST /al/api/v1/dashboard/review-screen/draft
  • GET /al/api/v1/dashboard/jobs/{job_id}
  • GET /al/api/v1/dashboard/openapi.json
  • GET /al/api/v1/dashboard/docs

Async mode for long-running jobs​

Many endpoints support async processing:

  • include mode=async (or async=true)
  • poll /jobs/{job_id}
  • optionally download artifacts from /jobs/{job_id}/download (ALDashboard)

To enable async workers, configure:

celery modules:
  - docassemble.ALWeaver.api_weaver_worker
  - docassemble.ALDashboard.api_dashboard_worker
  1. Use Coding with AI assistance for overall flow.
  2. Use MCP tools/list to discover available server capabilities.
  3. Use REST/MCP calls for concrete tasks (labeling, draft generation, validation).
  4. Apply the focused checklists in this section to review outputs before publishing.